Transfer Learning With GANs and Meta-Learning

This chapter explains how combined Transfer Learning, GANs, and Meta-Learning and the artificial intelligence fields where it can help solve challenging problems. In situations where information is scanty, characteristics of a model increase through Transfer Learning of information across contexts. GAN generator-discriminator networks generate realistic synthetic populations, whereas Meta-Learning demands prompt adaptation to address new learning problems with little samples. By answering these questions, this will help to discover how it solves data scarcity, enhances model generalization, and foster the advent of new medical imaging as well as creativity. The chapter enhances responsible innovation of the subject through acquisition of diverse cross-disciplinary knowledge. Lastly, the Transfer Learning, GANs, and Meta-Learning might develop a revolutionary AI that can increase private and public efficiency and improve creativity across many areas.